Machine Learning–Based Risk Model for Predicting Early Mortality After Surgery for Infective Endocarditis

BACKGROUND: The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy‐to‐use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. METHODS AND RESULTS: A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction, multiple valve involvement, tricuspid valve involvement, and vegetation >10 mm. The completed prediction model was tested in 2 separate cohorts for internal and external validation. The internal test cohort consisted of 125 patients independent of the development cohort, and the external test cohort consisted of 75 patients from another center. In the internal test cohort, the area under the curve was 0.813 (95% CI, 0.670–0.933) and in the external test cohort the area under the curve was 0.812 (95% CI, 0.606–0.956). The area under the curve was significantly higher than that of other ensemble learning models, logistic regression model, and European System for Cardiac Operative Risk Evaluation II (all, P<0.01). This model was used to develop an online, open‐access calculator (http://42.240.140.58:1808/). CONCLUSIONS: We constructed and validated an accurate and robust machine learning–based risk model to predict early mortality after surgery for infective endocarditis, which may help clinical decision‐making and improve outcomes.